| dc.description.abstract | In this study, we proposed two topics include the methods of facial and action analysis. A visual face feature extraction scheme using image processing techniques and visualize report is proposed. Five visual face features, face contours, face colors, smile lines, hairlines, and melanocytes, were detected, extracted, stored, and retrieved for aesthetic medicine. The results of facial analysis information can be long-term recorded for user retrieve. Perception and production of facial emotion is a kind of nonverbal communication between man and man. For example, the face of pain patients will be exposed to the painful emotion. Alarm messages will be sent to medical staff for notification when the system detects emotion of pain from patients. On the other hand, emotion recognition can be applied record of baby′ mood. This application can long-term recorded mood change in every day and report for the parents. There are many reasons could be effect baby′ emotion, such as diaper comfort, physical condition, the temperature of room temperature, etc. Thus, more researchers increasingly interested in developing algorithms for automatic recognition of facial emotion in still image and videos. However, most existing methods for facial emotion recognition utilize off-the-shelf feature extraction methods for classi?cation. Recently, video based human motion analysis and recognition has attracted a great deal of attention due its potential application and wide usage in a variety of areas, such as video surveillance, human-computer interaction, video indexing, video surveillance, sport event analysis, customer attributes, and shopping behavior analysis, etc. Basically, either global or local visual features are used for human action analysis in many published methods. Generally, an action is considered as a volume of video data both in space and time domain. Global features own the global representation and discriminative power. However, they are sensitive to intra-class variation and deformation like the cluttering backgrounds and partial occlusion in action sequences. The accuracy rates will be impacted by the background distortion. Second, the local visual representation of actions is to extraction the local features from interested points in spatial and temporal domains. In addition to the likelihood of falling in this elderly group is relatively high and can be regarded as a life-threatening event. Behavioral analysis can be used to record the activity content for an elderly person or child in a particular area every day. In general, some action is very dangerous in some places for children, for example, run or jump, etc. In addition to monitoring the child’s dangerous behavior, the service can also record the normal behavior, includes wave, bend, walking, etc. Ensure that the elderly or children have enough activity content for healthy living in every day. In order to learn better features of spatiotemporal information for emotion and action representation. In our study, the proposed unsupervised single-layer networks are applied to automatically learn the local feature, which can explicitly depict appearance and dynamic variations caused by facial emotion and human action. To combination the properties of the local visual representation and learning based model automated to extract feature. The local visual representation robust to intra-class variability caused by scale, pose changes, and occlusion, etc. The learning based model and be able to avoid the handcrafted features computed from a local cuboid around interest points. This method is also similar to the bag-of-features feature, including local feature extraction, vocabulary generation, feature vector representation and pooling, etc. Finally, we use a non-linear SVM with RBF kernel to recognize the facial emotion (human action). In addition, falling can cause severe harm to senior citizens. The ideal time for rescuing is immediately after the fall. However, falls are not always detected immediately, therefore detection in real time, using video surveillance systems, could save human life. Nowadays, digital cameras have been installed everywhere. Human activity is monitored using cameras connected to intelligent programs. An alarm can be sent to the administrator when an abnormal event occurs. In this paper, a manifold multi-view-based learning algorithm is proposed for detecting falling events. This algorithm is able to detect people falling in any direction. First, walking patterns at a normal speed are modeled by a locality preserving projection (LPP). Since the duration of a fall cannot easily be segmented from a video, partial temporal windows are matched with the normal walking patterns. The Hausdorff distances are calculated for comparison. In the experiments, falls were effectively detected using the proposed method. | en_US |